Python for Data Science & Machine Learning
Online course by Data Science and Machine Learning Specialist | Free PDF Certificate | Lifetime Access | CPD Accredited
Course Central
Summary
- Certificate of completion - Free
- Tutor is available to students
Overview
This Python for Data Science & Machine Learning course is designed for professionals who want to thrive in their profession. This course covers all the essential skills and knowledge needed to become specialised in this sector. You will learn real-life knowledge and expertise from industry experts and practitioners.
This Python for Data Science & Machine Learning 3 course bundle consists of the following courses:
- Course 1: Python for Data Science & Machine Learning
- Course 2: Leadership & Management
- Course 3: Time Management
The course starts with the basics of Data Science & Machine Learning and gradually shares expertise. Upon course completion, you will get a complete idea of this subject with key concepts, strategies regarding its use and in-depth knowledge. This is completely an online course so you can access this course from any part of the world with a smart device and the internet.
By the end of this course, you will get complete knowledge and marketable skills. The course also comes with CPD Accredited certificates, adding extra value to your resume and helping you stand out in the job market. Enrol on this course today and fast-track your career!
What will you Learn?
- Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
- How to create a resume and land your first job as a Data Scientist
- How to write complex Python programs for practical industry scenarios
- Learn to use NumPy for Numerical Data
- Supervised vs Unsupervised Machine Learning
- Machine Learning Concepts and Algorithms
- Use Python to clean, analyze, and visualize data
- Statistics for Data Science
- Learn data cleaning, processing, wrangling and manipulation
- How to use Python for Data Science
- Learn Plotting in Python (graphs, charts, plots, histograms etc)
- Machine Learning and its various practical applications
- Learn Regression, Classification, Clustering and Sci-kit learn
- K-Means Clustering
- Building Custom Data Solutions
- Probability and Hypothesis Testing
Why Choose Python for Data Science & Machine Learning course?
- Conducted by industry experts
- Get Instant E-certificate
- Fully online, interactive course with Professional voice-over
- Developed by qualified professionals
- Self-paced learning and laptop, tablet, smartphone friendly
- Tutor Support
And you will also get these gifts
- Free PDF Certificate
- Lifetime Course Access
CPD
Course media
Description
Course Curriculum
Introduction
- Who is This Course For?
- Data Science + Machine Learning Marketplace
- Data Science Job Opportunities
- Data Science Job Roles
- What is a Data Scientist?
- How To Get a Data Science Job
- Data Science Projects Overview
Data Science & Machine Learning Concepts
- Why We Use Python?
- What is Data Science?
- What is Machine Learning?
- Machine Learning Concepts & Algorithms
- What is Deep Learning?
- Machine Learning vs Deep Learning
Python For Data Science
- What is Programming?
- Why Python for Data Science?
- What is Jupyter?
- What is Google Colab?
- Python Variables, Booleans and None
- Getting Started with Google Colab
- Python Operators
- Python Numbers & Booleans
- Python Strings
- Python Conditional Statements
- Python For Loops and While Loops
- Python Lists
- More about Lists
- Python Tuples
- Python Dictionaries
- Python Sets
- Compound Data Types & When to use each one? 00:12:00
- Python Functions
- Object Oriented Programming in Python
Statistics for Data Science
- Intro To Statistics
- Descriptive Statistics
- Measure of Variability
- The measure of Variability Continued
- Measures of Variable Relationship
- Inferential Statistics
- Measure of Asymmetry
- Sampling Distribution
Probability & Hypothesis Testing
- What Exactly is Probability?
- Expected Values
- Relative Frequency
- Hypothesis Testing Overview
NumPy Data Analysis
- Intro NumPy Array Data Types
- NumPy Arrays
- NumPy Arrays Basics
- NumPy Array Indexing
- NumPy Array Computations
- Broadcasting
Pandas Data Analysis
- Introduction to Pandas
- Introduction to Pandas Continued
Python Data Visualization
- Data Visualization Overview
- Different Data Visualization Libraries in Python
- Python Data Visualization Implementation
Machine Learning
- Introduction To Machine Learning
Data Loading & Exploration
- Exploratory Data Analysis
Data Cleaning
- Feature Scaling
- Data Cleaning
Feature Selecting and Engineering
- Feature Engineering
Linear and Logistic Regression
- Linear Regression Intro
- Gradient Descent
- Linear Regression + Correlation Methods
- Linear Regression Implementation
- Logistic Regression
K Nearest Neighbors
- KNN Overview
- parametric vs non-parametric models
- EDA on Iris Dataset
- The KNN Intuition
- Implement the KNN algorithm from scratch
- Compare the result with the sklearn library
- Hyperparameter tuning using the cross-validation
- The decision boundary visualization
- Manhattan vs Euclidean Distance
- Feature scaling in KNN
- Curse of dimensionality
- KNN use cases
- KNN pros and cons
Decision Trees
- Decision Trees Section Overview
- EDA on Adult Dataset
- What is Entropy and Information Gain?
- The Decision Tree ID3 algorithm from scratch Part 1
- The Decision Tree ID3 algorithm from scratch Part 2
- The Decision Tree ID3 algorithm from scratch Part 3
- ID3 – Putting Everything Together
- Evaluating our ID3 implementation
- Compare with sklearn implementation
- Visualizing the tree
- Plot the feature's importance
- Decision Trees Hyper-parameters
- Pruning
- [Optional] Gain Ration
- Decision Trees Pros and Cons
- [Project] Predict whether income exceeds $50K/yr – Overview
Ensemble Learning and Random Forests
- Ensemble Learning Section Overview
- What is Ensemble Learning?
- What is Bootstrap Sampling?
- What is Bagging?
- Out-of-Bag Error (OOB Error)
- Implementing Random Forests from scratch Part 1
- Implementing Random Forests from scratch Part 2
- Compare with sklearn implementation
- Random Forests Hyper-Parameters
- Random Forests Pros and Cons
- What is Boosting?
- AdaBoost Part 1
- AdaBoost Part 2
Support Vector Machines
- SVM Outline
- SVM intuition
- Hard vs Soft Margins
- C hyper-parameter
- Kernel Trick
- SVM – Kernel Types
- SVM with Linear Dataset (Iris)
- SVM with Non-linear Dataset
- SVM with Regression
- [Project] Voice Gender Recognition using SVM
K-means
- Unsupervised Machine Learning Intro
- Unsupervised Machine Learning Continued
- Data Standardization
PCA
- PCA Section Overview
- What is PCA?
- PCA Drawbacks
- PCA Algorithm Steps (Mathematics)
- Covariance Matrix vs SVD
- PCA – Main Applications
- PCA – Image Compression
- PCA Data Preprocessing
- PCA – Biplot and the Screen Plot
- PCA – Feature Scaling and Screen Plot
- PCA – Supervised vs Unsupervised
- PCA – Visualization
Data Science Career
- Creating A Data Science Resume
- Data Science Cover Letter
- How to Contact Recruiters
- Getting Started with Freelancing
- Top Freelance Websites
- Personal Branding
- Networking Do’s and Don’ts
- Importance of a Website
Certificates
Course Central is proud to offer a Certificate of Completion to all who complete courses successfully. Course Central tracks the learner’s course progress. However, the learner is responsible for validating the completion and understanding of the course. All Certificates of Completion can be validated from the Course Central website using the validation code.
Transcripts
A Transcript for the course with completed module details can be requested for as little as £4.99. Please note that all course Certificates and Transcripts will be titled as published on the Course Central platform.
Who is this course for?
This course is ideal for those who work in or aspire to work in the following professions:
- Data Scientist
- Data Analyst
- Software Developer
- Data Engineer
- Anyone who wants to learn Data Science & Machine Learning with Python.
Requirements
-
Students should have basic computer skills
-
Students would benefit from having prior Python Experience but not necessary
Career path
This training course will lead you to many different career opportunities, here are few prospects:
- Python Programmer £59,237 per annum
- Python Software Engineer- £60,000 per annum
- Data Scientist - £73,420 per annum
Questions and answers
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Certificates
Certificate of completion
Digital certificate - Included
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